Topological data analysis (TDA) has emerged as powerful machinery in machine learning (ML), allowing us to extract complementary information on the observed objects, especially, from graph-structured data. In particular, TDA has become quite popular in various ML tasks, ranging from bioinformatics [1]}, [2]},
finance [3]}, [4]}, material science [5]},
biosurveillance [6]}, [7]}, network analysis [8]}, [9]}, as well as insurance and agriculture [10]}, [11]} (see the literature overviews [12]}, [13]} and the TDA applications library [14]}).
Recently there has emerged a highly active research area that combines the PH machinery with geometric deep learning (GDL) methods [15]}, [16]}, [17]}.
